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Fix typo: indinces -> indices (#8159)
* Fix typo: indinces -> indices * Fix some more * Fix some more * Fix some more * Fix CI
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Santiago Castro
GitHub
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c83cec44f8
commit
fdf893c441
@ -122,7 +122,7 @@ def get_default_model(targeted_task: Dict, framework: Optional[str], task_option
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Args:
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targeted_task (:obj:`Dict` ):
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Dictionnary representing the given task, that should contain default models
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Dictionary representing the given task, that should contain default models
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framework (:obj:`str`, None)
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"pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.
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@ -150,9 +150,7 @@ def get_default_model(targeted_task: Dict, framework: Optional[str], task_option
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else:
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# XXX This error message needs to be updated to be more generic if more tasks are going to become
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# parametrized
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raise ValueError(
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'The task defaults can\'t be correctly selectionned. You probably meant "translation_XX_to_YY"'
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)
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raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"')
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if framework is None:
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framework = "pt"
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@ -695,7 +693,7 @@ class Pipeline(_ScikitCompat):
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Internal framework specific forward dispatching
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Args:
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inputs: dict holding all the keyworded arguments for required by the model forward method.
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inputs: dict holding all the keyword arguments for required by the model forward method.
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return_tensors: Whether to return native framework (pt/tf) tensors rather than numpy array
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Returns:
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@ -874,7 +872,7 @@ class TextGenerationPipeline(Pipeline):
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args (:obj:`str` or :obj:`List[str]`):
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One or several prompts (or one list of prompts) to complete.
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return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to include the tensors of predictions (as token indinces) in the outputs.
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Whether or not to include the tensors of predictions (as token indices) in the outputs.
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return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to include the decoded texts in the outputs.
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clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
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@ -1710,7 +1708,7 @@ class QuestionAnsweringPipeline(Pipeline):
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question (:obj:`str` or :obj:`List[str]`):
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One or several question(s) (must be used in conjunction with the :obj:`context` argument).
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context (:obj:`str` or :obj:`List[str]`):
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One or several context(s) associated with the qustion(s) (must be used in conjunction with the
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One or several context(s) associated with the question(s) (must be used in conjunction with the
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:obj:`question` argument).
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topk (:obj:`int`, `optional`, defaults to 1):
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The number of answers to return (will be chosen by order of likelihood).
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@ -1959,7 +1957,7 @@ class SummarizationPipeline(Pipeline):
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return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to include the decoded texts in the outputs
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return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to include the tensors of predictions (as token indinces) in the outputs.
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Whether or not to include the tensors of predictions (as token indices) in the outputs.
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clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to clean up the potential extra spaces in the text output.
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generate_kwargs:
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@ -2077,7 +2075,7 @@ class TranslationPipeline(Pipeline):
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args (:obj:`str` or :obj:`List[str]`):
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Texts to be translated.
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return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to include the tensors of predictions (as token indinces) in the outputs.
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Whether or not to include the tensors of predictions (as token indices) in the outputs.
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return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to include the decoded texts in the outputs.
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clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
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@ -2188,7 +2186,7 @@ class Text2TextGenerationPipeline(Pipeline):
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args (:obj:`str` or :obj:`List[str]`):
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Input text for the encoder.
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return_tensors (:obj:`bool`, `optional`, defaults to :obj:`False`):
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Whether or not to include the tensors of predictions (as token indinces) in the outputs.
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Whether or not to include the tensors of predictions (as token indices) in the outputs.
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return_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
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Whether or not to include the decoded texts in the outputs.
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clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`False`):
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@ -2253,8 +2251,8 @@ class Conversation:
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:class:`~transformers.ConversationalPipeline`. The conversation contains a number of utility function to manage the
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addition of new user input and generated model responses. A conversation needs to contain an unprocessed user input
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before being passed to the :class:`~transformers.ConversationalPipeline`. This user input is either created when
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the class is instantiated, or by calling :obj:`conversional_pipeline.append_response("input")` after a conversation
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turn.
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the class is instantiated, or by calling :obj:`conversational_pipeline.append_response("input")` after a
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conversation turn.
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Arguments:
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text (:obj:`str`, `optional`):
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@ -2671,8 +2669,8 @@ def check_task(task: str) -> Tuple[Dict, Any]:
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- :obj:`"conversational"`
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Returns:
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(task_defaults:obj:`dict`, task_options: (:obj:`tuple`, None)) The actual dictionnary required to initialize
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the pipeline and some extra task options for parametrized tasks like "translation_XX_to_YY"
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(task_defaults:obj:`dict`, task_options: (:obj:`tuple`, None)) The actual dictionary required to initialize the
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pipeline and some extra task options for parametrized tasks like "translation_XX_to_YY"
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"""
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@ -89,7 +89,7 @@ class T5Tokenizer(PreTrainedTokenizer):
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extra_ids (:obj:`int`, `optional`, defaults to 100):
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Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
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accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
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indexed from the end of the vocabulary up to beginnning ("<extra_id_0>" is the last token in the vocabulary
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indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
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like in T5 preprocessing see `here
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<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
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additional_special_tokens (:obj:`List[str]`, `optional`):
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@ -100,7 +100,7 @@ class T5TokenizerFast(PreTrainedTokenizerFast):
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extra_ids (:obj:`int`, `optional`, defaults to 100):
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Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
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accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
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indexed from the end of the vocabulary up to beginnning ("<extra_id_0>" is the last token in the vocabulary
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indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
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like in T5 preprocessing see `here
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<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
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additional_special_tokens (:obj:`List[str]`, `optional`):
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